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Case Study

Challenge: Commercially available superalloys are the outcome of several years of empirical research and development. Even though these superalloys have good properties, they do not necessarily have the optimal balance of properties required for specific engineering applications.

Solution: Computational design tool, Alchemite, incorporates uncertainty to allow alloys to be designed with the greatest probability of meeting a design specification containing many different material properties. Alchemite combines experimental data with computational thermodynamic predictions to rapidly, reliably, and robustly identify the alloy composition that is most likely to meet a multi-criterion specification.

Outcome: Alchemite predicted that the new nickel-base alloy offered an ideal compromise between its properties for disc applications and seven of these properties were experimentally verified, demonstrating that it has better yield stress and oxidation resistance than commercially available alternatives. The capability to quickly discover materials computationally using Alchemite empowered engineers to rapidly optimize bespoke materials for a specific applications, bringing materials into the heart of the design process. Alchemite has also been used to design a nickel-base alloy for a combustor liner, and two Mo-based alloys for forging tools.

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